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Field
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. Expertise is required or highly desired in one or more of the following areas: algorithms, analytical derivation, data analysis, coding, or mathematical modeling. Strong programming skills are highly desired
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-term project. We are looking for a software engineer to develop new features and extend the capabilities of a real-time neural data processing and decoding platform. This includes optimizing GPU
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programming skills in C++ and/or Python; experience with high-performance or real-time computing, e.g. GPU, multi-core, embedded, is desirable Prior experience with SOFA is a clear advantage; experience with
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++ Knowledge of GPU programming. Experience in application performance profiling. Desired qualifications: Experience from working on quantum computing Experience in FPGA development. Contract terms This postdoc
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’ experience post-Ph.D. Expertise in power systems and power electronics. Experience in C/C++, Matlab, and Python programming. Experience in power systems software like PSCAD, PSS/e. Preferred Qualifications
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role We are seeking a highly motivated PhD student to perform fundamental research and to conceive truly sparse solutions (on both, CPU and GPU) for dynamic sparse training, aiming to cut the training
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fields, applying advanced techniques such as large-scale data processing and GPU-accelerated computing. Access to state-of-the-art research facilities and a new GPU cluster. Collaborative and inclusive
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duties as required by Head of Service/School 11. To contribute to the enhancement of the University’s international profile in line with the University’s Strategic Plan, World Changers Together. Knowledge
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approaches, the application of meta learning, and the integration of convex optimization layers Increase inference efficiency (e.g., GPU acceleration) and assess the applicability domain of learned algorithms
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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow